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Applying the K-Means Algorithm in Big Raw Data Sets with Hadoop and MapReduce
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Author(s): Ilias K. Savvas (TEI of Larissa, Greece), Georgia N. Sofianidou (TEI of Larissa, Greece)and M-Tahar Kechadi (University College Dublin, Ireland)
Copyright: 2014
Pages: 24
Source title:
Big Data Management, Technologies, and Applications
Source Author(s)/Editor(s): Wen-Chen Hu (University of North Dakota, USA)and Naima Kaabouch (University of North Dakota, USA)
DOI: 10.4018/978-1-4666-4699-5.ch002
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Abstract
Big data refers to data sets whose size is beyond the capabilities of most current hardware and software technologies. The Apache Hadoop software library is a framework for distributed processing of large data sets, while HDFS is a distributed file system that provides high-throughput access to data-driven applications, and MapReduce is software framework for distributed computing of large data sets. Huge collections of raw data require fast and accurate mining processes in order to extract useful knowledge. One of the most popular techniques of data mining is the K-means clustering algorithm. In this study, the authors develop a distributed version of the K-means algorithm using the MapReduce framework on the Hadoop Distributed File System. The theoretical and experimental results of the technique prove its efficiency; thus, HDFS and MapReduce can apply to big data with very promising results.
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